Knowledge catalogue
Meet NVIDIA Nemotron 3.5 Super VL Our new open model combines reasoning and tool use with text, image, document, and video understanding for long-running AI agents. 120B total parameters with 12B acti
arXiv:2610.19928v1 Announce Type: new Abstract: Reasoning over infinitely long documents is becoming a reality with recurrent memory agents. However, increasing context length exacerbates the trade-of
arXiv:2610.20459v1 Announce Type: new Abstract: Chemical chain-of-thought can contain hallucinated structural and property claims. We thereby introduce MolLean, a process-verifiable language that grou
NVIDIA Nemotron 3.5 Super VL is now available on Ollama This model combines reasoning and visual understanding across a range of agent workflows. On a single GPU, this model delivers up to 5x higher t
NVIDIA’s open‑source Nemotron 3.5 Super VL is a 120 B parameter mixture‑of‑experts multimodal model that activates only 12 B parameters per token, enabling combined reasoning, instruction following, t
arXiv:2610.14570v1 Announce Type: new Abstract: Large language models (LLMs) support social simulation, but faithfully reproducing real users requires grounding both structured platform decisions and
arXiv:2610.19108v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have been widely applied in embodied agents and robotics for vision tasks. However, MLLMs suffer from limitat
arXiv:2610.15097v1 Announce Type: new Abstract: Diffusion language models (DLMs) offer new opportunities for controllable text generation. However, their controllability, defined as the ability to sat
arXiv:2605.25437v2 Announce Type: replace Abstract: Visual reasoning through reinforcement learning with verifiable rewards (RLVR) has achieved remarkable progress. However, when dealing with multi-so
arXiv:2604.02341v2 Announce Type: replace Abstract: Mathematical reasoning in large language models has improved substantially with reinforcement learning using verifiable rewards, where final answers
arXiv:2610.14439v1 Announce Type: new Abstract: Large multimodal models (LMMs) need to understand how entities move and interact as a scene develops. This requires following the same entities over tim
arXiv:2610.13722v1 Announce Type: new Abstract: Legal language understanding requires reasoning over semi-structured regulatory documents, yet standard pre-training objectives do not explicitly target
arXiv:2610.12060v1 Announce Type: new Abstract: Processing long visual token sequences from high-resolution images makes multi-step reasoning computationally expensive for multimodal Large Language Mo
arXiv:2610.09876v1 Announce Type: cross Abstract: Diffusion models generate realistic images but often fail on visual reasoning tasks, such as filling in a Sudoku or drawing the path through a maze. W
arXiv:2610.09759v1 Announce Type: new Abstract: Flow matching enables language generation in few steps, but whether additional integration steps improve reasoning remains unclear. We prove that a flow
arXiv:2610.10118v1 Announce Type: cross Abstract: Long-horizon reasoning demands access to earlier information at a manageable generation cost. Full-history attention incurs growing storage and comput
arXiv:2610.07739v1 Announce Type: new Abstract: Post-discharge risk prediction from electronic health records (EHRs) is difficult because many dependencies that link discharge-time observations to dow
arXiv:2610.07835v1 Announce Type: new Abstract: LLM-based multi-agent systems (MAS) have demonstrated strong capabilities in solving complex problems across diverse domains. Recently, the dynamic orch
arXiv:2610.08726v1 Announce Type: cross Abstract: Egocentric human data offer a path to scaling robot learning beyond costly robot demonstrations, yet the embodiment gap makes raw human trajectories a
arXiv:2508.10020v2 Announce Type: replace-cross Abstract: Enhancing LLM reasoning in federated settings is nontrivial due to stringent computational, communication, and privacy constraints, especially
arXiv:2610.08585v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly relied upon to support ambient documentation and clinical reasoning. Here we examine the impact of a failu
arXiv:2610.08138v1 Announce Type: new Abstract: Legal intelligence aims to support reliable decision-making across long-horizon legal processes involving evolving case states and multiple roles. Howev
arXiv:2610.04286v1 Announce Type: cross Abstract: Distributed Energy Resource (DER) environments rely on network communication protocols to coordinate control commands, measurements, and device states
arXiv:2610.05023v1 Announce Type: new Abstract: We introduce Self-Saliency, a method for training Vision-Language Models (VLMs) to increase the alignment between their visual attention and the image r
arXiv:2610.06413v1 Announce Type: new Abstract: Thinking-enabled vision-language models (VLMs) report ever-higher accuracy on spatial benchmarks, yet final-answer scores cannot reveal whether a correc
arXiv:2610.05974v1 Announce Type: new Abstract: Reinforcement learning can substantially improve a reasoning teacher, but it is unclear which of those improvements survive when the teacher supervises
arXiv:2610.05025v1 Announce Type: new Abstract: Dual-system Vision-Language-Action (VLA) models improve real-time robotic control by pairing a slow, reasoning-capable generalist with a fast specialist
arXiv:2610.02525v1 Announce Type: new Abstract: Long-horizon research agents must decide both how to investigate and what to investigate next as evidence accumulates. This is hard to learn because suc
arXiv:2610.03326v1 Announce Type: new Abstract: Diffusion language model (dLLM) compression faces a known challenge because calibration is typically performed on clean, fully visible activations, wher
arXiv:2610.03664v1 Announce Type: new Abstract: Autoregressive (AR) video models excel at causal generation, but their reliance on next-chunk prediction confines them to a short-sighted, reactive para
arXiv:2610.02968v1 Announce Type: new Abstract: Large language models (LLMs) can infer user preferences from interaction histories and reviews, yet the rationales they generate may not reflect the inf
arXiv:2610.03548v1 Announce Type: new Abstract: Generating progressively harder reasoning problems requires synthesis procedures that adapt as the task distribution evolves. Existing task-level recurs
arXiv:2610.02695v1 Announce Type: cross Abstract: Reliable large language models (LLMs) must not only produce accurate answers but also express confidence that faithfully reflects their probability of
arXiv:2610.01166v1 Announce Type: cross Abstract: Cardiovascular magnetic resonance (CMR), including cine imaging, is a reference standard for the noninvasive assessment of cardiac morphology and vent
arXiv:2601.07148v4 Announce Type: replace-cross Abstract: Tool use, such as web search, has become a standard capability even in freely available large language models (LLMs). However, existing benchm
arXiv:2609.39135v1 Announce Type: new Abstract: Physical reasoning from video requires inferring latent physical properties and dynamics beyond direct observation. Direct VLM inference remains unrelia
arXiv:2609.34211v2 Announce Type: replace Abstract: In financial fraud detection, rich semantic context can provide important evidence for transaction behavior modeling and fraud reasoning. However, p
arXiv:2609.38699v1 Announce Type: new Abstract: A cumulative token cap can fall inside a mathematical derivation, forcing a test-time controller to choose between stopping at the cap (strict) and allo
arXiv:2609.39566v1 Announce Type: new Abstract: Foundation models can serve as clinical agents through tool-use harnesses. However, conventional medical benchmarks assess reasoning over preselected ev
arXiv:2609.40079v1 Announce Type: cross Abstract: While recent Multimodal Large Language Models (MLLMs) have shown promise in affective computing, their reasoning capabilities are largely confined to
arXiv:2609.39727v1 Announce Type: new Abstract: Cooperative language-model agents must coordinate over long horizons and adapt to changing environments and to partners with unfamiliar conventions, yet
arXiv:2609.38541v1 Announce Type: new Abstract: Instruction-guided video editing has made significant progress, yet existing methods use multimodal large language models (MLLMs) primarily as semantic
arXiv:2609.39846v1 Announce Type: cross Abstract: We investigate the problem of role-capability leakage (RCL), in which a role-prompted reasoning model generates convincing in-role text while continui
arXiv:2609.36826v1 Announce Type: new Abstract: Video generation models have demonstrated emerging zero-shot capabilities for visual reasoning, perception, and other vision tasks. However, diffusion-b
arXiv:2609.36219v1 Announce Type: new Abstract: Perspective taking is a fundamental component of spatial intelligence, requiring models interpret spatial relations from a specified viewpoint, such as
arXiv:2609.35694v2 Announce Type: replace Abstract: Continuous diffusion generates complete reasoning solutions through iterative refinement in latent space. We introduce the Continuous Embedding Diff
arXiv:2609.36982v1 Announce Type: cross Abstract: Knowledge concept tagging aims to assign specific concept or topic labels to educational content, which is essential for both educators and learners i
arXiv:2609.37918v1 Announce Type: new Abstract: Reasoning across videos requires aligning events, matching identities, comparing motion, and integrating partial observations. Evaluating these capabili
arXiv:2609.37818v1 Announce Type: cross Abstract: Empathetic spoken dialogue requires models to use both what is said and how it is said to decide how to respond. Explicit CoT can improve paralinguist
arXiv:2609.37656v1 Announce Type: new Abstract: Large vision-language models (LVLMs) exhibit strong reasoning capabilities, yet the visual and textual evidence supporting the generated responses remai
arXiv:2609.32157v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models for autonomous driving produce natural-language reasoning alongside predicted trajectories, but whether this reasoni
arXiv:2609.32870v1 Announce Type: cross Abstract: Self-play proposer--solver methods improve reasoning by generating tasks and learning from verified solutions. However, for evidence-identifiable task
arXiv:2609.31911v1 Announce Type: new Abstract: Medical report generation has made significant progress with the rise of modern vision-language models and the growing availability of large-scale medic
arXiv:2609.33190v1 Announce Type: new Abstract: Driving decisions depend on both where to focus and how to act on what is seen. Effective driving reasoning must establish which objects matter, where t
arXiv:2609.33585v1 Announce Type: new Abstract: The rapid proliferation of highly realistic AI-Generated Content (AIGC) necessitates robust and interpretable detection mechanisms. However, existing de
arXiv:2609.35350v1 Announce Type: cross Abstract: While Large Reasoning Models (LRMs) excel at complex reasoning, alignment through reinforcement learning often induces systemic overconfidence. In pro
arXiv:2609.34572v1 Announce Type: cross Abstract: Reasoning language models that can call tools must decide during inference whether to answer unaided or delegate. Any self-reflection mechanism for th
arXiv:2609.32470v1 Announce Type: new Abstract: Large reasoning models (LRMs) often suffer from overconfidence when expressing their uncertainty. Confidence-aware reinforcement learning (RL) offers a
arXiv:2609.34975v1 Announce Type: new Abstract: Group Relative Policy Optimization (GRPO) improves language-model reasoning by comparing verified rewards among multiple solution rollouts for each quer
arXiv:2609.35228v1 Announce Type: new Abstract: Latent test-time scaling improves reasoning by refining hidden states during inference, but existing methods typically apply a single scalar reward to a